Can we use attributes and metrics of songs to predict their likelihood of winning a Grammy?

What common traits, if any, do award winning songs contain? Can we look at intrinsic traits of songs, combined with metrics defined by Spotify, to determine award winning musical features? In this paper, we break down our data collection, data processing, and data analysis of a dataset of roughly 1,000 popular songs, both award-winning and not.

The Data

Data Collection and Selection

JOAN TO DO: Write how we chose the 1,000 songs originally; how we ended up with 867; and which Grammy winners we stuck with (only Grammy winners, or also Grammy nominees?)

# Set the working directory to this file's folder
library("rstudioapi")
setwd(dirname(getActiveDocumentContext()$path))
load("final_df_n_str.RData")

Sys.setenv(LANG = "en") 

# Load necessary libraries
library(pROC)
library(MASS)
library(ROSE)
library(confintr)
library(ggplot2)
library(correlation)
library(corrplot)
library(class)
library(caret)
library(glmnet)
# Selecting the relevant variables
data = final_df_n_str
data = data[,c("track_name", "artist_name", "IsWinner", "Year","year",
               "followers", "acousticness", "danceability", "duration_ms",
               "energy", "instrumentalness", "key", "liveness", "loudness",
               "mode", "tempo", "time_signature", "valence")]

# Merge the two year variable
data$Year[data$Year == "Undefined"] <- data$year[data$Year == "Undefined"]
data = data[,c("track_name","artist_name", "IsWinner", "Year", "followers",
               "acousticness", "danceability", "duration_ms",
               "energy", "instrumentalness", "key", "liveness", "loudness",
               "mode", "tempo", "time_signature", "valence")]

# Eliminating duplicates
data$track_name == "Closing Time"
data$track_name == "Smells Like Teen Spirit"
data$track_name == "Don't Wanna Fight"
data[914, ]
data[789,]
data[669,]

data = data[-c(669, 789, 914),]

sum(data$Year < 1992)
nrow(data)
data = data[!data$Year < 1992,]

# Creating row names

names = paste0(data$track_name, " - ", data$artist_name)

# Eliminating unusable variables
data = data[,c("IsWinner", "Year", "followers", "acousticness",
               "danceability", "duration_ms", "energy",
               "instrumentalness", "key", "liveness", "loudness", "mode",
               "tempo", "time_signature", "valence")]
data = cbind(names = names, data)

# Casting variables
data$IsWinner[data$IsWinner == "Winner"] = 1
data$IsWinner[data$IsWinner == "Nominee"] = 1
data$IsWinner[data$IsWinner == "Nothing"] = 0
data$IsWinner = as.integer(data$IsWinner)
data$Year = as.integer(data$Year)
data$mode = as.factor(data$mode)
data$key = as.factor(data$key)
data$time_signature = as.factor(data$time_signature)

# Giving row names
summary(data)
summary(data$IsWinner)

Explanation of Variables

In order to perform analysis of songs, we decided to use metrics that are intrinsic to music as well as artificial metrics created and measured by the music streaming giant Spotify. The intrinsic metrics we used were: duration, musical key, modality (major or minor key), tempo, time signature, and genre. Spotify also uses what they call “audio features” (in the table below) to perform their own analysis of songs when creating playlists, suggesting music, etc. We used these professionally manufactured metrics to bolster the intrinsic metrics and increase our insight into what might make a song award-winning.

Audio Feature Definition
Acousticness A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
Danceability Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
Energy Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
Instrumentalness Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
Liveness Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
Loudness The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 db.
Speechiness Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
Valence A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

Although Spotify does not openly share how they determine these metrics, we found them suitable to assist in our analysis.

As a final processing step, we split the data into training and test datasets. The training dataset contains 80% of the original dataset, and the remaining 20% of the data in the test dataset will be used to test tour model against after we have trained it. It is very important to test the model on never-before-seen data to determine not only how well the model performs, but also how well the model can generalize.

# Splitting training and test set
training_size = floor(0.8 * nrow(data))
set.seed(42)
train_ind = sample(seq_len(nrow(data)), size = training_size)
training_set = data[train_ind,]
test_set = data[-train_ind,]

summary(training_set)
##     names              IsWinner           Year        followers       
##  Length:693         Min.   :0.0000   Min.   :1992   Min.   :    2597  
##  Class :character   1st Qu.:0.0000   1st Qu.:2001   1st Qu.:  868777  
##  Mode  :character   Median :0.0000   Median :2010   Median : 2350118  
##                     Mean   :0.1876   Mean   :2009   Mean   : 4338356  
##                     3rd Qu.:0.0000   3rd Qu.:2018   3rd Qu.: 5615666  
##                     Max.   :1.0000   Max.   :2023   Max.   :44692754  
##                                                                       
##   acousticness        danceability    duration_ms          energy      
##  Min.   :0.0000032   Min.   :0.130   Min.   :  78591   Min.   :0.0975  
##  1st Qu.:0.0016900   1st Qu.:0.419   1st Qu.: 206413   1st Qu.:0.6040  
##  Median :0.0278000   Median :0.522   Median : 237800   Median :0.7570  
##  Mean   :0.1553733   Mean   :0.512   Mean   : 251635   Mean   :0.7182  
##  3rd Qu.:0.2050000   3rd Qu.:0.607   3rd Qu.: 278267   3rd Qu.:0.8820  
##  Max.   :0.9880000   Max.   :0.894   Max.   :1355938   Max.   :0.9960  
##                                                                        
##  instrumentalness        key         liveness         loudness       mode   
##  Min.   :0.00e+00   9      : 95   Min.   :0.0157   Min.   :-18.148   0:203  
##  1st Qu.:4.90e-06   2      : 94   1st Qu.:0.0989   1st Qu.: -8.086   1:490  
##  Median :3.21e-04   7      : 84   Median :0.1240   Median : -6.253          
##  Mean   :6.25e-02   0      : 81   Mean   :0.2004   Mean   : -6.645          
##  3rd Qu.:1.49e-02   11     : 68   3rd Qu.:0.2320   3rd Qu.: -4.767          
##  Max.   :8.95e-01   4      : 58   Max.   :0.9980   Max.   : -1.574          
##                     (Other):213                                             
##      tempo        time_signature    valence      
##  Min.   : 48.58   1:  2          Min.   :0.0494  
##  1st Qu.: 99.19   3: 37          1st Qu.:0.3050  
##  Median :121.14   4:649          Median :0.4640  
##  Mean   :123.28   5:  5          Mean   :0.4725  
##  3rd Qu.:141.93                  3rd Qu.:0.6310  
##  Max.   :205.85                  Max.   :0.9730  
## 
# Checking if the ratio is preserved
sum(data$IsWinner == 1)/ sum(data$IsWinner == 0)
## [1] 0.2159888
sum(training_set$IsWinner == 1)/ sum(training_set$IsWinner == 0)
## [1] 0.2309059
training_set
## # A tibble: 693 × 16
##    names   IsWinner  Year followers acousticness danceability duration_ms energy
##    <chr>      <int> <int>     <int>        <dbl>        <dbl>       <int>  <dbl>
##  1 Nightm…        0  2010   6262809     0.000318        0.554      374453  0.949
##  2 I'd Do…        0  1993   1034322     0.465           0.366      718600  0.561
##  3 Patien…        1  2022   4802169     0.000195        0.318      441402  0.87 
##  4 Someda…        1  2006   6137375     0.254           0.533      295560  0.59 
##  5 I Know…        0  2020   1775452     0.33            0.323      344693  0.323
##  6 Find M…        1  2021   4416749     0.256           0.873      293849  0.809
##  7 Weak -…        0  2017   2923531     0.118           0.67       201159  0.643
##  8 Walk O…        1  2001  11148674     0.00379         0.528      296240  0.832
##  9 Black …        1  2018   2341237     0.197           0.558      259893  0.902
## 10 Spectr…        0  2012   6399322     0.00225         0.578      218190  0.946
## # ℹ 683 more rows
## # ℹ 8 more variables: instrumentalness <dbl>, key <fct>, liveness <dbl>,
## #   loudness <dbl>, mode <fct>, tempo <dbl>, time_signature <fct>,
## #   valence <dbl>

Exploratory Data Analysis

Relationship Between Independent Variables

At first, we took a look at the continuous variables.

attach(training_set)
## The following object is masked _by_ .GlobalEnv:
## 
##     names
# Correlations between continuous variables
cor_matrix = cor(training_set[,c(-1, -2, -10, -13, -15)])
corrplot(cor_matrix)

pairs(training_set[,c(-1, -2, -10, -13, -15)], lower.panel = panel.smooth)

WHY CAN’T I GET THIS PAIRS() PDF TO INSERT?? Maybe it is inserted, but too big??

knitr::include_graphics("yourPlot.pdf", error = FALSE)

We looked at the association measure for categorical variables utilizing Cramer’s V, which is a normalized version of the chi-square statistic.

(CRISTIAN IS THIS CORRECT? EXPAND?)

# Association measure for categorical variables (Cramer's V is a normalized 
# version of the chi-square statistics)
cramersv(matrix(c(as.numeric(key), as.numeric(mode)), ncol = 2))
## [1] 0.3275984
cramersv(matrix(c(as.numeric(key), as.numeric(time_signature)), ncol = 2))
## [1] 0.305952
cramersv(matrix(c(as.numeric(mode), as.numeric(time_signature)), ncol = 2))
## [1] 0.1425218

Next, we looked for associations between each of the categorical variables (Key, Mode, and Time Signature) and all of the continuous variables. Some of these were significant, meaning… (ASK CRISTIAN ABOUT THIS)

# Association between continuous and categorical variables

# Key
fol_key.aov <- aov(followers ~ key)
summary(fol_key.aov) # SIGNIFICANT
##              Df    Sum Sq   Mean Sq F value Pr(>F)  
## key          11 9.557e+14 8.688e+13   2.129 0.0167 *
## Residuals   681 2.780e+16 4.082e+13                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aco_key.aov <- aov(acousticness ~ key)
summary(aco_key.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## key          11   0.76 0.06888   1.154  0.316
## Residuals   681  40.65 0.05969
dan_key.aov <- aov(danceability ~ key)
summary(dan_key.aov) # SIGNIFICANT
##              Df Sum Sq Mean Sq F value Pr(>F)  
## key          11  0.368 0.03343   1.911 0.0351 *
## Residuals   681 11.913 0.01749                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dur_key.aov <- aov(duration_ms ~ key)
summary(dur_key.aov)
##              Df    Sum Sq   Mean Sq F value Pr(>F)  
## key          11 1.302e+11 1.184e+10   1.671 0.0758 .
## Residuals   681 4.825e+12 7.086e+09                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ene_key.aov <- aov(energy ~ key)
summary(ene_key.aov) # SIGNIFICANT
##              Df Sum Sq Mean Sq F value Pr(>F)  
## key          11  0.806 0.07330   1.926 0.0334 *
## Residuals   681 25.922 0.03806                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ins_key.aov <- aov(instrumentalness ~ key)
summary(ins_key.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## key          11  0.503 0.04574   1.696 0.0701 .
## Residuals   681 18.362 0.02696                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
liv_key.aov <- aov(liveness ~ key)
summary(liv_key.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## key          11  0.437 0.03974   1.184  0.294
## Residuals   681 22.859 0.03357
loud_key.aov <- aov(loudness ~ key)
summary(loud_key.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## key          11    109   9.871   1.467  0.139
## Residuals   681   4583   6.730
tem_key.aov <- aov(tempo ~ key)
summary(tem_key.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## key          11  12755  1159.5   1.384  0.176
## Residuals   681 570619   837.9
val_key.aov <- aov(valence ~ key)
summary(val_key.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## key          11   0.77 0.07024   1.486  0.132
## Residuals   681  32.18 0.04725
# Mode
fol_mode.aov <- aov(followers ~ mode)
summary(fol_mode.aov) # SIGNIFICANT
##              Df    Sum Sq   Mean Sq F value Pr(>F)  
## mode          1 2.630e+14 2.630e+14    6.38 0.0118 *
## Residuals   691 2.849e+16 4.123e+13                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aco_mode.aov <- aov(acousticness ~ mode)
summary(aco_mode.aov) # SIGNIFICANT
##              Df Sum Sq Mean Sq F value Pr(>F)  
## mode          1   0.32  0.3200   5.382 0.0206 *
## Residuals   691  41.09  0.0595                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dan_mode.aov <- aov(danceability ~ mode)
summary(dan_mode.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## mode          1  0.012 0.01171    0.66  0.417
## Residuals   691 12.269 0.01775
dur_mode.aov <- aov(duration_ms ~ mode)
summary(dur_mode.aov)
##              Df    Sum Sq   Mean Sq F value Pr(>F)
## mode          1 9.401e+07 9.401e+07   0.013  0.909
## Residuals   691 4.955e+12 7.171e+09
ene_mode.aov <- aov(energy ~ mode)
summary(ene_mode.aov) # SIGNIFICANT
##              Df Sum Sq Mean Sq F value  Pr(>F)   
## mode          1  0.374  0.3737   9.798 0.00182 **
## Residuals   691 26.354  0.0381                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ins_mode.aov <- aov(instrumentalness ~ mode)
summary(ins_mode.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## mode          1  0.017 0.01729   0.634  0.426
## Residuals   691 18.848 0.02728
liv_mode.aov <- aov(liveness ~ mode)
summary(liv_mode.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## mode          1  0.038 0.03803    1.13  0.288
## Residuals   691 23.258 0.03366
loud_mode.aov <- aov(loudness ~ mode)
summary(loud_mode.aov) # SIGNIFICANT
##              Df Sum Sq Mean Sq F value  Pr(>F)   
## mode          1     59   59.19   8.828 0.00307 **
## Residuals   691   4633    6.70                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tem_mode.aov <- aov(tempo ~ mode)
summary(tem_mode.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## mode          1    928   928.2   1.101  0.294
## Residuals   691 582445   842.9
val_mode.aov <- aov(valence ~ mode)
summary(val_mode.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## mode          1   0.00 0.00049    0.01   0.92
## Residuals   691  32.95 0.04769
# Time signature
fol_time.aov <- aov(followers ~ time_signature)
summary(fol_time.aov)
##                 Df    Sum Sq   Mean Sq F value Pr(>F)
## time_signature   3 1.400e+13 4.667e+12   0.112  0.953
## Residuals      689 2.874e+16 4.171e+13
aco_time.aov <- aov(acousticness ~ time_signature)
summary(aco_time.aov) # SIGNIFICANT
##                 Df Sum Sq Mean Sq F value   Pr(>F)    
## time_signature   3   1.27  0.4238   7.275 8.28e-05 ***
## Residuals      689  40.14  0.0583                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dan_time.aov <- aov(danceability ~ time_signature)
summary(dan_time.aov) # SIGNIFICANT
##                 Df Sum Sq Mean Sq F value  Pr(>F)   
## time_signature   3  0.239 0.07964   4.557 0.00359 **
## Residuals      689 12.042 0.01748                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dur_time.aov <- aov(duration_ms ~ time_signature)
summary(dur_time.aov) # SIGNIFICANT
##                 Df    Sum Sq   Mean Sq F value   Pr(>F)    
## time_signature   3 1.156e+11 3.855e+10   5.488 0.000993 ***
## Residuals      689 4.840e+12 7.024e+09                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ene_time.aov <- aov(energy ~ time_signature)
summary(ene_time.aov) # SIGNIFICANT
##                 Df Sum Sq Mean Sq F value   Pr(>F)    
## time_signature   3   0.74 0.24678   6.543 0.000229 ***
## Residuals      689  25.99 0.03772                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ins_time.aov <- aov(instrumentalness ~ time_signature)
summary(ins_time.aov) # SIGNIFICANT
##                 Df Sum Sq Mean Sq F value  Pr(>F)   
## time_signature   3   0.34 0.11337   4.217 0.00573 **
## Residuals      689  18.52 0.02689                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
liv_time.aov <- aov(liveness ~ time_signature)
summary(liv_time.aov)
##                 Df Sum Sq Mean Sq F value Pr(>F)
## time_signature   3  0.021 0.00686   0.203  0.894
## Residuals      689 23.276 0.03378
loud_time.aov <- aov(loudness ~ time_signature)
summary(loud_time.aov) # SIGNIFICANT
##                 Df Sum Sq Mean Sq F value  Pr(>F)   
## time_signature   3     78  25.887   3.865 0.00927 **
## Residuals      689   4614   6.697                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tem_time.aov <- aov(tempo ~ time_signature)
summary(tem_time.aov) # SIGNIFICANT
##                 Df Sum Sq Mean Sq F value Pr(>F)  
## time_signature   3   7794  2598.1    3.11 0.0259 *
## Residuals      689 575579   835.4                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
val_time.aov <- aov(valence ~ time_signature)
summary(val_time.aov) # SIGNIFICANT
##                 Df Sum Sq Mean Sq F value Pr(>F)  
## time_signature   3   0.52 0.17279   3.671 0.0121 *
## Residuals      689  32.43 0.04707                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The fact that x, y, z, etc. were significant leads us to etc. etc. etc. CHAT WITH CRISTIAN ABOUT THIS TOO.

TALK ABOUT PARTIAL CORRELAtIONS

# Partial correlations
correlation(training_set[,c(-1, -2, -10, -13, -15)], partial = TRUE)
## # Correlation Matrix (pearson-method)
## 
## Parameter1       |       Parameter2 |         r |         95% CI | t(691) |         p
## -------------------------------------------------------------------------------------
## Year             |        followers |     -0.09 | [-0.16, -0.02] |  -2.37 | 0.580    
## Year             |     acousticness |      0.15 | [ 0.07,  0.22] |   3.94 | 0.004**  
## Year             |     danceability |      0.14 | [ 0.07,  0.21] |   3.77 | 0.007**  
## Year             |      duration_ms |     -0.08 | [-0.15,  0.00] |  -2.07 | > .999   
## Year             |           energy |     -0.03 | [-0.10,  0.05] |  -0.74 | > .999   
## Year             | instrumentalness |      0.13 | [ 0.05,  0.20] |   3.33 | 0.035*   
## Year             |         liveness | -8.88e-03 | [-0.08,  0.07] |  -0.23 | > .999   
## Year             |         loudness |      0.24 | [ 0.17,  0.31] |   6.58 | < .001***
## Year             |            tempo |      0.10 | [ 0.02,  0.17] |   2.58 | 0.340    
## Year             |          valence |     -0.12 | [-0.19, -0.05] |  -3.18 | 0.057    
## followers        |     acousticness |     -0.04 | [-0.11,  0.04] |  -1.03 | > .999   
## followers        |     danceability | -7.23e-03 | [-0.08,  0.07] |  -0.19 | > .999   
## followers        |      duration_ms |      0.03 | [-0.05,  0.10] |   0.74 | > .999   
## followers        |           energy |     -0.06 | [-0.14,  0.01] |  -1.71 | > .999   
## followers        | instrumentalness |     -0.06 | [-0.14,  0.01] |  -1.65 | > .999   
## followers        |         liveness |     -0.06 | [-0.13,  0.02] |  -1.47 | > .999   
## followers        |         loudness |      0.11 | [ 0.04,  0.18] |   2.89 | 0.144    
## followers        |            tempo |      0.01 | [-0.06,  0.09] |   0.33 | > .999   
## followers        |          valence |     -0.09 | [-0.16, -0.01] |  -2.28 | 0.687    
## acousticness     |     danceability |     -0.03 | [-0.10,  0.05] |  -0.72 | > .999   
## acousticness     |      duration_ms |     -0.07 | [-0.15,  0.00] |  -1.92 | > .999   
## acousticness     |           energy |     -0.45 | [-0.51, -0.39] | -13.31 | < .001***
## acousticness     | instrumentalness |      0.03 | [-0.05,  0.10] |   0.67 | > .999   
## acousticness     |         liveness |      0.09 | [ 0.01,  0.16] |   2.35 | 0.593    
## acousticness     |         loudness |     -0.13 | [-0.20, -0.05] |  -3.40 | 0.028*   
## acousticness     |            tempo |      0.04 | [-0.04,  0.11] |   0.99 | > .999   
## acousticness     |          valence |      0.07 | [-0.01,  0.14] |   1.79 | > .999   
## danceability     |      duration_ms |     -0.10 | [-0.18, -0.03] |  -2.75 | 0.215    
## danceability     |           energy |     -0.15 | [-0.22, -0.08] |  -3.99 | 0.003**  
## danceability     | instrumentalness |      0.04 | [-0.03,  0.11] |   1.06 | > .999   
## danceability     |         liveness |     -0.13 | [-0.20, -0.05] |  -3.36 | 0.032*   
## danceability     |         loudness |     -0.01 | [-0.09,  0.06] |  -0.33 | > .999   
## danceability     |            tempo |     -0.31 | [-0.37, -0.24] |  -8.50 | < .001***
## danceability     |          valence |      0.53 | [ 0.47,  0.58] |  16.23 | < .001***
## duration_ms      |           energy |      0.06 | [-0.02,  0.13] |   1.49 | > .999   
## duration_ms      | instrumentalness |      0.16 | [ 0.09,  0.23] |   4.35 | < .001***
## duration_ms      |         liveness |     -0.04 | [-0.11,  0.04] |  -0.98 | > .999   
## duration_ms      |         loudness |     -0.08 | [-0.16, -0.01] |  -2.22 | 0.771    
## duration_ms      |            tempo |      0.01 | [-0.06,  0.09] |   0.30 | > .999   
## duration_ms      |          valence |     -0.15 | [-0.22, -0.07] |  -3.86 | 0.005**  
## energy           | instrumentalness |      0.16 | [ 0.09,  0.23] |   4.30 | < .001***
## energy           |         liveness |      0.15 | [ 0.08,  0.23] |   4.11 | 0.002**  
## energy           |         loudness |      0.62 | [ 0.57,  0.66] |  20.64 | < .001***
## energy           |            tempo |      0.04 | [-0.03,  0.12] |   1.10 | > .999   
## energy           |          valence |      0.26 | [ 0.18,  0.32] |   6.95 | < .001***
## instrumentalness |         liveness |     -0.06 | [-0.14,  0.01] |  -1.67 | > .999   
## instrumentalness |         loudness |     -0.18 | [-0.25, -0.11] |  -4.85 | < .001***
## instrumentalness |            tempo |      0.06 | [-0.01,  0.14] |   1.70 | > .999   
## instrumentalness |          valence |     -0.09 | [-0.16, -0.02] |  -2.40 | 0.554    
## liveness         |         loudness |     -0.08 | [-0.15,  0.00] |  -2.02 | > .999   
## liveness         |            tempo |     -0.05 | [-0.13,  0.02] |  -1.38 | > .999   
## liveness         |          valence |      0.01 | [-0.06,  0.09] |   0.29 | > .999   
## loudness         |            tempo |     -0.03 | [-0.10,  0.05] |  -0.70 | > .999   
## loudness         |          valence |  7.25e-04 | [-0.07,  0.08] |   0.02 | > .999   
## tempo            |          valence |      0.16 | [ 0.09,  0.23] |   4.33 | < .001***
## 
## p-value adjustment method: Holm (1979)
## Observations: 693
# Plots of variables with the largest partial correlation
ggplot(data = training_set, aes(danceability, valence)) + geom_jitter(color = "blue")

ggplot(data = training_set, aes(loudness, energy)) + geom_jitter(color = "blue")

ggplot(data = training_set, aes(acousticness, energy)) + geom_jitter(color = "blue")

#Weird song veeeeeeeeeeeeeeeeeeeeeeeery long 
which.max(data$duration_ms)
## [1] 448
data[504, ]
## # A tibble: 1 × 16
##   names    IsWinner  Year followers acousticness danceability duration_ms energy
##   <chr>       <int> <int>     <int>        <dbl>        <dbl>       <int>  <dbl>
## 1 Drops -…        0  2014    886702        0.853        0.703      173627  0.237
## # ℹ 8 more variables: instrumentalness <dbl>, key <fct>, liveness <dbl>,
## #   loudness <dbl>, mode <fct>, tempo <dbl>, time_signature <fct>,
## #   valence <dbl>

TALK ABOUT WHY SOME MAY BE SIGNIFICANT; WHAT THAT MEANS.

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